Personal devices, such as computers, phones, personal digital assistants and the like have gained wide popularity in recent years. As technology improves, these devices have become increasingly smaller in size and highly portable. In fact, wireless, portable devices of various types now commonly communicate with one another allowing users flexibility of use and facilitating data, voice and audio communication. To this end, networking of mobile or portable and wireless devices is required.
With regard to the wireless networking of personal devices, a particular modem, namely modems adapted to the up-coming IEEE 8302.11n industry standard, are anticipated to be commonly employed. This standard allows for an array of antennas to be placed inside or nearby the personal device and a radio frequency (RF) semiconductor device receives signal or data through the array and an analog-to-digital converter, typically located within the personal device, converts the received signal to baseband range. Thereafter, a baseband processor is employed to process and decode the received signal to the point of extracting raw data, which may be files transferred remotely and wireless, from another personal device or similar equipment with the use of a transmitter within the transmitting PC.
To do so, pointing of the array of antennas, which is essentially multiple antennas, hence the name multi-input-multi-output (MIMO), to the desired location to maximize reception and transmission quality is an issue. For example, data or information rate throughput, signal reception and link range are improved. The latest IEEE802.11n standard currently being developed includes advanced multi-antenna techniques in order to process parallel data streams simultaneously in order to increase throughput capability, and improve link quality by “smartly” transmitting and receiving the RF signals.
MIMO has drawn attention as the technique for enabling a high-capacity (high-speed) data communication by efficiently using a frequency band. The MIMO is the technique to transmit separate data streams from a plurality of antennas of a transmitter by using a plurality of antennas in both of the transmission and the reception, that is to say, by using the transmitter having a plurality of antennas and the receiver having a plurality of antennas, and individually separate a plurality of transmission signals (data streams) mixed on a transmission path from the signal received by each receiving antenna of the receiver by using a transmission path (channel) estimate value, thereby improving a transmission rate without requiring an enlargement of the frequency band.
FIG. 1 shows the general structure of a typical MIMO OFDM system according to the prior art. The MIMO transmitter 100 has multiple antennas 110A-110C each capable of transmitting independent signals to a MIMO receiver 120 which is also equipped with multiple receive antennas 130A-130B. The transmitter 100 may comprise a forward error correction (FEC) code encoder 101, an interleaver 102, a MIMO constellation Mapper 103, an OFDM MEMO IFFT 104 and an analog and RF unit 105. The MIMO receiver 120 may comprise an RF and analog unit 121, a MIMO FFT 122, a MIMO demodulator 123 (like a slicer), a de-interleaver 124 and a FEC Decoder 125, all of which are used to convert the incoming RF signals into spatial streams representing bits of information sent over the channel. The MIMO demodulator receives a plurality of spatial streams of bits, and converts them into information in a format required by the FEC decoder. In some MIMO systems the demodulator performs hard decision and delivers information bits whereas in other systems the decoder delivers soft output for further soft decoding to be performed in a Viterbi decoder, low density parity check (LDPC) decoder or the like. Here the demodulator 123 is a maximum likelihood (ML) demodulator that might employ a K-Best or Fixed Sphere Demodulator algorithm.
The ML demodulator for a MIMO receiver operates by comparing the received signal vector with all possible noiseless received signals corresponding to all possible transmitted signals. Under certain assumptions, this receiver achieves optimal performance in the sense of maximizing the probability of correct data detection. The main idea behind the a sub-optimal implementation like the K-Best algorithm or the Fixed Sphere algorithm is to perform a search over only a small subset of all possible signals located around the received signal vector. The size of this search may be determined by a combination of performance and hardware requirements. This reduced search space ensures that the demodulator complexity is not only reduced, but also fixed over time—a major advantage for hardware implementation. In order for such a search to operate efficiently, a key point is to order the antennas in such a way that most of the points considered relate to transmit antennas with the poorest signal-to-noise (SNR) conditions. Antennas with, higher SNR conditions are much more likely to be detected correctly, based only on the received signal.
The ML demodulator has exponential complexity in the number of spatial streams being received. Essentially, the complexity of this decoder increases exponentially with the number of transmit streams, making it impossible to implement for large array sizes and high order digital modulation schemes.